Terrence Alsup is a PhD candidate advised by Benjamin Peherstorfer. He is interested in uncertainty quantification as well as the intersection of numerical analysis and machine learning. His work was on multi-fidelity methods for importance sampling and applications to Bayesian inverse problems. He successfully defended his thesis “Trading off deterministic approximations and sampling for multifidelity Bayesian inference” in December 2022. The thesis covers two multifidelity methods: context-aware importance sampling and a multilevel version of Stein variational gradient descent. Both methods leverage cheaper, but less accurate, surrogate models to speedup expensive computations that arise when performing Bayesian inference. He know works in industry for FinTech.
Paul Beckman is a PhD student working with Mike O’Neil. He is interested in numerical methods for spatial statistics and uncertainty quantification. His current research is in fast algorithms for rank-structured covariance matrices. He is also the recepient of the DOE CSGF fellowship.
Sylvie Bronsard works with mentor Miranda Holmes-Cerfon.
Natalie Frank works on problems in data science with mentors Jonathan Niles-Weed and Miranda Holmes-Cerfon. She is developing methods to compute high-dimensional volumes, and will apply these methods to study the geometry of configuration spaces of jammed particles, the volumes of Birkhoff polytopes, and to aid in sampling from mixtures of Gaussians that are used in Bayesian machine learning.
Anya Katsevich works with mentor Jonathan Weare. She is exploring hydrodynamic limits of a model of crystal surface relaxation (solid-on-solid). She is the recepient of the DOE CSGF fellowship.
Karina Koval obtained her PhD in May of 2021, advised by Georg Stadler. Her primary interests include numerical methods, optimization under uncertainty and uncertainty quantification. Her research involves inverse problems governed by partial differential equations. Here are slides from thesis presentation on "Optimal experimental design for Bayesian inverse problems governed by PDE models with uncertainty with application to subsurface flow and tsunami equations."
Freddy Law is a PhD student working with Antoine Cerfon. He is interested in numerical methods for PDE as well as uncertainty quantification, primarily for problems arising in computational physics. His current work is in the simulation of turbulent plasma flows. He is also the recepient of the DoD NDSEG fellowship.
Ondrej Maxian works with mentors Aleksandar Donev and Alex Mogilner on computational methods for cell mechanics. In particular, his research focuses on modeling and simulation of cross-linked actin networks/gels. He is the receipient of an NSF GRF fellowship.
Alexandre Milewski works with Miranda Holmes-Cerfon on developing numerical methods to efficiently simulate collections of particles with short-ranged interactions. He is studying the sensitive interplay between hydrodynamic lubrication forces and the range of the attractive interaction potential.
Olivia Pomerenk works in the Applied Math Lab with Leif Ristroph. Her work is related to biological fluid dynamics, including collective locomotion of flapping flyers as relevant to formation flight of birds, as well as collective pumping and filtration by oysters.
Anthony S Trubiano
Anthony Trubiano obtained his PhD in May of 2021 under the supervision of Miranda Holmes-Cerfon. He is interested in the mathematical modeling of physical systems in various fields of science and the methods of applied and computational mathematics in analyzing them. Particular interests include stochastic analysis, statistical mechanics, Monte Carlo methods, and other numerical solution algorithms. Here are slides from thesis presentation on "Modeling and Overcoming Thermodynamic-Kinetic Tradeoffs for Self-Assembling Colloidal Chains." He will be a postdoc at Brandeis University's MRSEC center starting in the Fall of 2021.
Scott Weady studied the fluid dynamics of erosion and ice melting in the Applied Math Lab under the supervision of Leif Ristroph. He also co-lead the 2021 AM-SURE program. He successfully defended his thesis
Rob Webber obtained his Ph.D. in May of 2021, advised by Jonathan Weare. He studies Monte Carlo methods with applications in atmospheric science, electronic structure theory, molecular dynamics, and linear algebra. Here are slides from his thesis defense on the topic of "Non-intrusive Monte Carlo methods for high-dimensional estimation." He will be a postdoctoral fellowship at Caltech under the supervision of Joel Tropp starting in the Fall of 2021.